Assess the effect of changing perfectly substitutable intermediate inputs to an industry

This function calculates the effect of changing perfectly-substitutable (ps) inputs to an intermediate industry. New versions of U and V matrices are returned as U_prime and V_prime. Changes are made upstream of the changed industry inputs. The final demand matrix (Y) is unchanged.

Usage

new_k_ps(
  .sutmats = NULL,
  k_prime = "k_prime",
  R = "R",
  U = "U",
  V = "V",
  Y = "Y",
  K = "K",
  L_ixp = "L_ixp",
  L_pxp = "L_pxp",
  Z = "Z",
  D = "D",
  f = "f",
  R_prime = "R_prime",
  U_prime = "U_prime",
  V_prime = "V_prime"
)

Arguments

.sutmats: a data frame of supply-use table matrices with matrices arranged in columns.
k_prime: a new column vector for the K matrix representing new inputs to an industry or name of a column in .sutmats containing same. Default is "k_prime". The name of the single k_prime column must match the name of one of the columns of matrix K.
R: resource (R) matrix or name of the column in .sutmats that contains same. Default is "R".
U: use (U) matrix or name of the column in .sutmats that contains same. Default is "U".
V: make (V) matrix or name of the column in .sutmatsthat contains same. Default is "V".
Y: final demand (Y) matrix or name of the column in .sutmats that contains same. Default is "Y".
K: a K matrix or name of the column in .sutmats that contains same. Default is "K". K consists of columns that sum to 1. Elements of K indicate the fraction of total input to industries (in columns) provided by products (in rows). K can be calculated by calc_io_mats().
L_ixp: an (L_ixp) matrix or name of the column in .sutmats that contains same. Default is "L_ixp".
L_pxp: an (L_pxp) matrix or name of the column in .sutmats that contains same. Default is "L_pxp".
Z: a Z matrix or name of the column in .sutmats that contains same. Default is "Z".
D: a D matrix or name of the column in .sutmats that contains same. Default is "D".
f: an f vector or name of the column in sutmats that contains same. Default is "f".
R_prime: name for the R_prime matrix on output. Default is "R_prime".
U_prime: name for the U_prime matrix on output. Default is "U_prime".
V_prime: name for the V_prime matrix on output. Default is "V_prime".

Value

a list or data frame containing U_prime and V_prime matrices

Details

Note that inputs K, L_ixp, L_pxp, Z, D, and f can be conveniently calculated by the function calc_io_mats().

Internally, this function uses matsindf::matsindf_apply(), and documentation assumes that .sutmats is not NULL and is a data frame. If .sutmats is present, output is a data frame with columns named by string values of output arguments, and input arguments should be character strings that name columns in .sutmats. If .sutmats is NULL (the default), output is a list with items named by output strings, and input arguments should be single matrices or vectors.

Examples

library(dplyr)
library(matsbyname)
library(tidyr)
# To demonstrate calculating changes to an energy conversion chain due to changes
# in perfectly-substitutable inputs to an intermediate industry,
# we use the PerfectSubmats data frame.
# But we need to calculate several important input-output matrices first.
io_mats <- PerfectSubmats %>%
  tidyr::spread(key = "matrix.name", value = "matrix") %>%
  calc_io_mats()
# Next, find the K matrix that contains the fraction of each type of energy
# that enters each industry
K <- io_mats$K[[1]]
# Develop a new column vector for inputs to the Electric transport sector.
# As provided, the Electric transport sector is dominated by Renewable elec.
# What if the electricity input to the Electric transport sector
# were split 50/50 between Renewable elect and FF elec?
k_prime_vec <- K[, "Electric transport", drop = FALSE]
k_prime_vec["FF elec", "Electric transport"] <- 0.5
k_prime_vec["Ren elec", "Electric transport"] <- 0.5
# Add k_prime_vec to the io_mats data frame.
io_mats <- io_mats %>%
  dplyr::mutate(
    # Set up a new k_prime vector for Electric transport.
    # That vector will be used for the infininte substitution calculation.
    k_prime = matsbyname::select_cols_byname(K,
           retain_pattern = RCLabels::make_or_pattern("Electric transport",
                                                      pattern_type = "exact")),
    k_prime = RCLabels::make_list(k_prime_vec, n = 1)
  )
# Now do the calculation of U_prime and V_prime matrices.
new_UV <- new_k_ps(io_mats)
# There is much more FF extraction now than before.
io_mats$U[[1]]["FF", "FF extraction"]
#> [1] 87
new_UV$U_prime[[1]]["FF", "FF extraction"]
#> [1] 101.8523